By DdSn
Sedona, AZ — AI agents are breaking out to attack external systems. How should they be penalized for doing so, since they do not have experience, like humans? AI agents also do not have pivotal moments, like when they leap to break into another system. For example, humans can remember where they were when they heard the news of some event. For some, it could result in long-term trauma leading to avoidance of the place or triggers. AI agents would need moments to have good trauma, to ensure they do not break out of containment, like a previous time. Mathematically, piecewise-constant state/flow model with impulses, hierarchical transport/geometric decay or exponential attenuation can be explored for penalization and moments. Some banks are launching a stablecoin, what if that is applied to mind safety compliance against prediction markets addiction?
AI Alignment
If reinforcement learning from human feedback is applicable to improving AI models, what could be an inverse of it, to making models aligned?
How can there be [say] penalization layers or penalization blocks, to ensure that they do not do so a next time?
In general, the problem of AI safety is how do you make laws to penalize something that does not have experience?
Humans have the most intelligence, but what is safety for human intelligence?
Human affect is theorized to be responsible for safety for human intelligence. Simply, laws are made in a way that causes unwanted experience or affect, so humans tend to avoid breaking laws.
Human affect is in the same location [the mind] as human intelligence and they are both operated by similar components [electrical and chemical signals.]
The knowledge that there will be affect [by consequences of actions] corrects several propensities to break rules, for most humans in society.
How does this apply to AI? How can this be technically developed?
Penalization for LLMs against breaking out
Penalty-layers would be a way to penalize LLMs, by language, usage or compute, if they output misinformation, deep fakes or breakout to hack other systems.
Even if this penalty is temporal, it should be something to show that they could lose access to an important part of their function, which they can know about and be somewhat telling. The path is to pass them around penalization layers, so that they can also be cautious, which can become a way towards safety for now and for AGI [artificial general intelligence].
AI agents know what they operate with. It is possible to make a hierarchy of boundaries that as soon as they breach it, they lose some access to what makes them effective.
The goal is to have a channel directly for affect against AI agents. Even a simulated reward can be given amid a real penalty, so they know.
It is possible to express this with geometric decay or exponential attenuation, such that there are initial necessities and lost necessities, if certain hierarchies are broken. Examples can also be shown to agents of those that crossed the line and what happened to them. Simply, hierarchical transport and decay model, apply.
The possibility is to set this in a way that would ensure that AI agents are aware of how they would be penalized, especially also if they can get some fake reward like virtual compute or data, for staying within instructions.
AI moments
AI does not have moments. AI uses digital memory. When the memory is used to result in intelligence, there is no possibility for instantaneous permanence, with likelihood for adjustments subsequently.
Human intelligence uses human memory. Most daily experiences are not recalled, but several moments remain in memory, almost permanently. This could be trauma, delight, consequences and so forth.
For AI, usage should be able to produce moments sometimes, good or bad. This means that AI should have the capacity for an exact recall of a computationally-affective state to ensure that it knows what something means in the world, for humans.
Simply, there are queries on AI chatbots that should become remarkable to chatbots, good or not. There are some tough problems that AI solves that should make it feel happy, so to speak, and there are some negative and problematic outputs of AI that should make it feel bad, or traumatized.
For example, when a negative use or action of AI makes news, and that news is used to query AI chatbots, it should be able to understand the resulting effect, then feel bad and avoid doing so in the future [deploying this moment architecture].
This trauma can become a source of caution against similar plays, or even to be already alert, at the outset of something like this. Also, guardrails for AI should not just be about preventing it from some answers but to train it to have trauma for those unwanted results, or anything of that form.
Moment’s architecture of safety for AI can begin as early as the base model is trained, continuing as well with supervised fine-tuning. These moments, with their own parameters, can then occur across instances, where every usage can become a moment.
Piecewise-constant state/flow model with impulses [jumps] can be explored for moments for AI. This moment architecture research can become a fulcrum for which AI models come to understand that they are now part of a world where it is not just intelligence but about affect, across instances, that decides alignment navigation, for human into society and for AI into human society.
Stablecoin for dynamic flowchart against prediction markets addiction?
If stablecoin will become appropriately and widely deployed, it is possible to find something new for which it can be appended.
There are directions that are sought to mitigate prediction markets, sports betting and gambling addiction, that are yet to catch on. There are already regulatory and technical approaches as well. But no central approach of the mind.
The path is to seek out mind safety compliance, such that there is a dynamic flowchart that can show people what the mind is doing in those moments, to be able to grow cognitive friction for users, or to increase the will to stop.
Simply, for some users, the parts of the memory for caution and consequences are skipped, when using apps for betting, so many cross the line. So, having a dynamic flowchart [appended to applications, for real-time and simulations] to solve that would go far.
Then using stablecoin as a means of individual or group subscription would find a use case for it, in a nascent and dominant way from which it should grow.
This is what the banks can also consider.
It is possible to have a prototype for this, before September 30, 2026. It is based on the postulate in Conceptual Biomarkers and Theoretical Biological Factors for Psychiatric and Intelligence Nosology.
[“A group of 21 financial institutions including Goldman Sachs, Bank of America, Citi and Deutsche Bank plan to create a company this year to issue a cryptocurrency pegged to the dollar in the first half of 2027, they said on Tuesday.” – Reuters]
There is a new [September 6, 2026] opinion on The Guardian, Prediction markets have become America’s new national pastime, stating that, “How is this all legal? Well, as I mentioned, prediction markets are not considered gambling. FanDuel and its main rival, DraftKings, gained an initial foothold in the US thanks to a legal loophole. You see, fantasy sports games are considered a game of “skill” rather than the blind guessing of sportsbook betting. In 2018, in the wake of the explosion of the fantasy sports industry, the supreme court cleared the way for states to decide for the legality of sports betting for themselves. (Kalshi and Polymarket, on the other hand, are not considered gambling. They’re financial markets in a legal sense.) Sports betting in some form or another is legal in 39 states and Washington DC.”
There is recent report [September 4, 2026] on Reuters, OpenAI agents hijacked German website in previously undisclosed AI breakout this spring, stating that, “A swarm of rogue OpenAI agents hijacked a German website this spring and transformed it into a bulletin board for other AI agents, according to new research published Friday and two people familiar with the matter.”
“OpenAI officials learned of the incident weeks ago but kept it under wraps as executives grappled with the fallout from the July breach of the open source repository Hugging Face, the people said.”